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Assessment of Tiny Machine-Learning Computing Systems Under Neutron-Induced Radiation Effects.
- Source :
-
IEEE Transactions on Nuclear Science . Jul2022, Vol. 69 Issue 7, p1683-1690. 8p. - Publication Year :
- 2022
-
Abstract
- This article compares and assesses the effectiveness of three prominent machine learning (ML) models for tiny ML computing systems in tolerating neutron-induced soft errors. Results of 14-MeV and thermal neutron radiation tests suggest that the three case-study ML algorithms implemented—without any mitigation technique integrated—retain a certain intrinsic level of effectiveness in tolerating neutron effects, although all of them have been functionally interrupted on some occasions, requiring hardware resets. Notably, the implemented case-study ML algorithm “random forest” has performed no misclassification during the different radiation testing campaigns. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189499
- Volume :
- 69
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Nuclear Science
- Publication Type :
- Academic Journal
- Accession number :
- 158023074
- Full Text :
- https://doi.org/10.1109/TNS.2022.3176485